Advances in computing hardware and software have fueled exponential growth in delivery of vast amounts of information due to increased improvements in computational and networking technologies. Also, advances in conventional data storage technologies provide an ability to store increasing amounts of generated data. Thus, improvements in computing hardware, software, network services, and storage have bolstered growth of Internet-based messaging applications, such as social networking platforms and applications, especially in an area of generating and sending information concerning products and services. Unfortunately, such technological improvements have contributed to a deluge of information that is so voluminous that any particular message may be drowned out in the sea of information. Consequently, providers of goods and services are typically inundated with messages concerning customer service-related matters via social networking platforms. Brand reputations and brand loyalty may be jeopardized if providers of goods and services are impeded from filtering through a multitude of messages to identify a relatively small number of critical messages.
In accordance with some conventional techniques, creators of content and information, such as manufacturers and merchants of products or services, have employed various techniques to review numerous messages to identify content that might be of critical nature. However, while functional, these techniques suffer a number of other drawbacks.
The above-described advancements in computing hardware and software have given rise to a relatively large number of communication channels through which information may be transmitted to the masses. For example, information may be transmitted via a great number of messages through text messages, website posts, social networking messages, and the like. However, social networking platforms are not well-suited to leverage social media to address customer service-related issues as social media platforms were initially formed to principally connect persons socially rather than commercially. For example, various conventional approaches to reviewing numerous social-related messages typically are resource intensive, requiring human reviewers to read a message and determine some type of dispositive action, which typically may be less repeatable and subject to various levels of skill and subjectivity applied to identifying messages that may be important to discover. Further, it is not unusual for the traditional approaches to consume relatively large quantities of computational resources and time, among other things.
Thus, what is needed is a solution for facilitating techniques to predict an action based on electronic messages, without the limitations of conventional techniques.